Suppose that \((Y^n,Y_{n+1})\)  are iid from \(\mathsf {P}_{Y|\theta }\) . [1]} showed that prediction can effectively be treated as a marginal inference problem, one where \(\theta \)  itself is a nuisance parameter. Their arguments involve the dimension-reduction strategies in [2]}, [3]}, but we omit these details here.
